Run
10594027

Run 10594027

Task 361901 (Supervised Classification) Weather Uploaded 07-08-2023 by said edsa
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,n ominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=skl earn.feature_selection._variance_threshold.VarianceThreshold,randomforestcl assifier=sklearn.ensemble._forest.RandomForestClassifier)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`.
sklearn.ensemble._forest.RandomForestClassifier(32)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(32)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(32)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(32)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(32)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(32)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(32)_random_state4989
sklearn.ensemble._forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(32)_warm_startfalse
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [1, 2]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 3]}}]
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose_feature_names_outtrue
sklearn.preprocessing._data.StandardScaler(17)_copytrue
sklearn.preprocessing._data.StandardScaler(17)_with_meantrue
sklearn.preprocessing._data.StandardScaler(17)_with_stdtrue
sklearn.preprocessing._encoders.OneHotEncoder(48)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(48)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(48)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(48)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(48)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(48)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(48)_sparse"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(48)_sparse_outputtrue
sklearn.feature_selection._variance_threshold.VarianceThreshold(12)_threshold0.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

Area under ROC curve
0.6222 ± 0.5
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6929
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3171 ± 0.5164
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1084 ± 0.3029
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.42 ± 0.1252
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4643 ± 0.0874
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7143 ± 0.3536
Cross-validation details (10-fold Crossvalidation)
Number of instances
14
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7056
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7143 ± 0.3536
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9413 ± 0.2576
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9046 ± 0.2461
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4795 ± 0.0916
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4833 ± 0.1564
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.008 ± 0.2828
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6444 ± 0.25
Cross-validation details (10-fold Crossvalidation)